Datasets:

LuminScience
/

AniTrack

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema_version: int64
dataset_id: string
display_name: string
short_name: string
status: string
redistribution: string
objective: string
core4_keypoints: list<item: string>
  child 0, item: string
nominal_totals: struct<frames: int64, instances: int64, valid_core4_coordinates: int64, fully_complete_instances: in (... 4 chars omitted)
  child 0, frames: int64
  child 1, instances: int64
  child 2, valid_core4_coordinates: int64
  child 3, fully_complete_instances: int64
split_policy: struct<seed: int64, mars_topview_pose: string, kumar_ofa_pose: string, lightning_pose_crim13: string (... 74 chars omitted)
  child 0, seed: int64
  child 1, mars_topview_pose: string
  child 2, kumar_ofa_pose: string
  child 3, lightning_pose_crim13: string
  child 4, sleap_mice_hc: string
  child 5, sleap_mice_of: string
  child 6, mouse_lockbox_top: string
image_materialization: struct<format: string, quality: int64, subsampling: int64, reason: string>
  child 0, format: string
  child 1, quality: int64
  child 2, subsampling: int64
  child 3, reason: string
bbox_policy: struct<method: string, scale: double, fallback: string>
  child 0, method: string
  child 1, scale: double
  child 2, fallback: string
openfield_leakage_gate: struct<held_out_dataset: string, expected_images: int64, abort_on_exact_decoded_pixel_match: bool, d (... 41 chars omitted)
  child 0, held_out_dataset: string
  child 1, expected_images: int64
  child 2, abort_on_exact_decoded_pixel_match: bool
  child 3, dhash_hamming_diagnostic_threshold: int64
sources: list<item: struct<id: string, license: string, view: string, frames: int64, instances: int64, valid_ (... 134 chars omitted)
  child 0, item: struct<id: string, license: string, view: string, frames: int64, instances: int64, valid_core4_coord (... 122 chars omitted)
      child 0, id: string
      child 1, license: string
      child 2, view: string
      child 3, frames: int64
      child 4, instances: int64
      child 5, valid_core4_coordinates: int64
      child 6, fully_complete_instances: int64
      child 7, camera_id: string
      child 8, annotated_rows: int64
      child 9, excluded_zero_core4_rows: int64
to
{'schema_version': Value('int64'), 'sources': List({'id': Value('string'), 'display_name': Value('string'), 'license_note': Value('string'), 'files': List({'name': Value('string'), 'url': Value('string'), 'size': Value('int64'), 'md5': Value('string'), 'unpack': Value('string')})})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema_version: int64
              dataset_id: string
              display_name: string
              short_name: string
              status: string
              redistribution: string
              objective: string
              core4_keypoints: list<item: string>
                child 0, item: string
              nominal_totals: struct<frames: int64, instances: int64, valid_core4_coordinates: int64, fully_complete_instances: in (... 4 chars omitted)
                child 0, frames: int64
                child 1, instances: int64
                child 2, valid_core4_coordinates: int64
                child 3, fully_complete_instances: int64
              split_policy: struct<seed: int64, mars_topview_pose: string, kumar_ofa_pose: string, lightning_pose_crim13: string (... 74 chars omitted)
                child 0, seed: int64
                child 1, mars_topview_pose: string
                child 2, kumar_ofa_pose: string
                child 3, lightning_pose_crim13: string
                child 4, sleap_mice_hc: string
                child 5, sleap_mice_of: string
                child 6, mouse_lockbox_top: string
              image_materialization: struct<format: string, quality: int64, subsampling: int64, reason: string>
                child 0, format: string
                child 1, quality: int64
                child 2, subsampling: int64
                child 3, reason: string
              bbox_policy: struct<method: string, scale: double, fallback: string>
                child 0, method: string
                child 1, scale: double
                child 2, fallback: string
              openfield_leakage_gate: struct<held_out_dataset: string, expected_images: int64, abort_on_exact_decoded_pixel_match: bool, d (... 41 chars omitted)
                child 0, held_out_dataset: string
                child 1, expected_images: int64
                child 2, abort_on_exact_decoded_pixel_match: bool
                child 3, dhash_hamming_diagnostic_threshold: int64
              sources: list<item: struct<id: string, license: string, view: string, frames: int64, instances: int64, valid_ (... 134 chars omitted)
                child 0, item: struct<id: string, license: string, view: string, frames: int64, instances: int64, valid_core4_coord (... 122 chars omitted)
                    child 0, id: string
                    child 1, license: string
                    child 2, view: string
                    child 3, frames: int64
                    child 4, instances: int64
                    child 5, valid_core4_coordinates: int64
                    child 6, fully_complete_instances: int64
                    child 7, camera_id: string
                    child 8, annotated_rows: int64
                    child 9, excluded_zero_core4_rows: int64
              to
              {'schema_version': Value('int64'), 'sources': List({'id': Value('string'), 'display_name': Value('string'), 'license_note': Value('string'), 'files': List({'name': Value('string'), 'url': Value('string'), 'size': Value('int64'), 'md5': Value('string'), 'unpack': Value('string')})})}
              because column names don't match

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AniTrack

The public data companion to LuminBench-AniTrack: mouse detection and Core-4 pose (nose, left_ear, right_ear, tail_base).

What is published here?

This initial release contains source-download manifests, semantic mappings, an acquisition script, and the training-data recipe. It does not contain image shards, per-image labels, pretrained weights, or a ready-to-load datasets.load_dataset() training table. The counts below describe the original prepared training views, not payloads currently hosted on this Hub repository.

Upstream images and labels must be acquired from their original publishers, under each source's terms. Public availability is not permission to relicense or mirror every source. See LICENSE.md.

Frozen recipe: Bridge-33K Rotating v2

Partition / training view	Frames	Mouse annotations	Use
Pose training	32,713	53,420	Gradient updates
Detector training	32,754	53,502	Gradient updates
External validation	2,662	4,801	Checkpoint selection
DLC historical test	263	285 boxes / 283 Core-4-evaluable mice	Final comparison only

The union before cleaning contains 27,826 external training frames plus 5,114 TopViewMouse training frames. The latter includes 1,012 Openfield training images; the disjoint 54 Openfield test images remain inside the frozen 263-image historical test. Historical test images must not be used to train, generate training pseudo-labels, select checkpoints, or tune thresholds.

Source families

Counts are upstream human-supervised frames across their available splits, before the AniTrack split and cleaning operations.

Source	Frames	Supervision / view	Terms and evidence
TopViewMouse5K v2	5,114 train + 263 test	Human 27-point union; partial Core-4; mostly top view	Constituent-source terms; no single annotator-count claim
MARS	15,000	Two mice, seven points, top view	CC-BY-NC-4.0; five-worker median labels
Kumar OFA	8,910	One mouse, twelve points, top view	Custom non-commercial terms; human labels, rater count unreported
Lightning Pose CRIM13	5,260 labeled centers	Two mice, seven points, top view	CC-BY-4.0; five-annotator median; 21,040 neighboring frames are not human-labeled centers
SLEAP mice_hc	1,474	Two mice, five points, overhead	Human labels; preserve dataset credits and verify redistribution terms
SLEAP mice_of	1,000	One to five mice, partial Core-4, below-floor	Human labels; not a strict-overhead source; verify redistribution terms
Mouse Lockbox	544 usable top-camera rows	One mouse, apparatus occlusions	CC-BY-4.0; human pose labels; only verified top camera used

Sampling, cleaning, and limitations

MARS, Kumar and CRIM13 are capped at 1,242 frames per epoch each, using seeded rotating windows. Smaller sources retain their eligible frames. Pose training rotates the selected target in multi-mouse images; detector training retains all valid boxes in a selected frame. The original pose virtual epoch contains 11,070 crops, with 600 epochs and global batch 64 (103,800 updates).

Human partial keypoints are masked, never filled with teacher predictions. Near-complete overlap is excluded from the isolated pose-crop view but retained for detector supervision when boxes remain useful. Empty records and exact duplicate annotations are removed. The original detector loader additionally skipped seven invalid normalized-label records; do not silently repair the published recipe and still call it an exact reproduction.

Published video/group splits are preserved where available. MARS lacks released session identifiers; its deterministic frame-hash split is not proof of session-independent generalization. TopViewMouse does not expose reliable session IDs either. SLEAP mice_of is a below-floor domain and should be reported separately in source-level evaluations.

Acquire the original archives

Clone this small metadata repository on the machine where you intend to store the data:

git clone https://huggingface.co/datasets/LuminScience/AniTrack
cd AniTrack
python3 download_sources.py --list
# Only after reviewing the upstream terms; downloads are explicit:
python3 download_sources.py --download --accept-source-terms \
  --output-root /your/data/raw

The script uses only Python's standard library, validates pinned publisher MD5 checksums (and sizes where available), and does not extract or alter the source archives. Existing valid files are reused; invalid existing files cause a hard failure. Use --sources mars_topview_pose to acquire a selected source. No training split is created by this acquisition step.

The manifests/ files preserve native layouts and Core-4 mappings. The external preparation configuration records the split and missing-label policies; recipe.json records the combined training contract. Conversion, split audits, and pose/detector training live in the companion code repository. Pin revisions of both repositories for any experiment. The historical external staging manifest filename containing gated refers to two now-directly-downloadable sources, not a Hub access gate.

Scope

This is a frame-level detection/pose resource, not newly annotated ground truth for persistent tracking identities. No new animal experiments were conducted to produce this collection. Cite the original datasets and respect their animal research, attribution, and usage requirements. Candidate datasets awaiting access or semantic review are not counted in this recipe.

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